Steps

0. Install and load libraries

1. Read in the data

There are 4 datasets to read in, Starbucks locations, Starbucks nutrition, US population by state, and US state abbreviations.

sb_locs <- read_csv("starbucks-locations.csv", show_col_types = FALSE)

sb_nutr <- read_csv("starbucks-menu-nutrition.csv", show_col_types = FALSE)

usa_pop <- read_csv("us_state_pop.csv", show_col_types = FALSE)

usa_states<-read_csv("states.csv", show_col_types = FALSE)

2. Look at the data

Inspect each dataset to look at variable names and ensure it was imported correctly

# no need to do any sort of cleaning (e.g. remove any rows), 
# but check that all data are imported correctly
dim(sb_locs)

## [1] 25600    13

colnames(sb_locs)

##  [1] "Brand"          "Store Number"   "Store Name"     "Ownership Type"
##  [5] "Street Address" "City"           "State/Province" "Country"       
##  [9] "Postcode"       "Phone Number"   "Timezone"       "Longitude"     
## [13] "Latitude"

summary(sb_locs)

##     Brand           Store Number        Store Name        Ownership Type    
##  Length:25600       Length:25600       Length:25600       Length:25600      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##  Street Address         City           State/Province       Country         
##  Length:25600       Length:25600       Length:25600       Length:25600      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##    Postcode         Phone Number         Timezone           Longitude      
##  Length:25600       Length:25600       Length:25600       Min.   :-159.46  
##  Class :character   Class :character   Class :character   1st Qu.:-104.67  
##  Mode  :character   Mode  :character   Mode  :character   Median : -79.35  
##                                                           Mean   : -27.87  
##                                                           3rd Qu.: 100.63  
##                                                           Max.   : 176.92  
##                                                           NA's   :1        
##     Latitude     
##  Min.   :-46.41  
##  1st Qu.: 31.24  
##  Median : 36.75  
##  Mean   : 34.79  
##  3rd Qu.: 41.57  
##  Max.   : 64.85  
##  NA's   :1

head(sb_locs)

## # A tibble: 6 × 13
##   Brand     `Store Number` `Store Name`  `Ownership Type` `Street Address` City 
##   <chr>     <chr>          <chr>         <chr>            <chr>            <chr>
## 1 Starbucks 47370-257954   Meritxell, 96 Licensed         Av. Meritxell, … Ando…
## 2 Starbucks 22331-212325   Ajman Drive … Licensed         1 Street 69, Al… Ajman
## 3 Starbucks 47089-256771   Dana Mall     Licensed         Sheikh Khalifa … Ajman
## 4 Starbucks 22126-218024   Twofour 54    Licensed         Al Salam Street  Abu …
## 5 Starbucks 17127-178586   Al Ain Tower  Licensed         Khaldiya Area, … Abu …
## 6 Starbucks 17688-182164   Dalma Mall, … Licensed         Dalma Mall, Mus… Abu …
## # ℹ 7 more variables: `State/Province` <chr>, Country <chr>, Postcode <chr>,
## #   `Phone Number` <chr>, Timezone <chr>, Longitude <dbl>, Latitude <dbl>

dim(sb_nutr)

## [1] 205   7

colnames(sb_nutr)

## [1] "Item"        "Category"    "Calories"    "Fat (g)"     "Carb. (g)"  
## [6] "Fiber (g)"   "Protein (g)"

summary(sb_nutr)

##      Item             Category            Calories        Fat (g)     
##  Length:205         Length:205         Min.   :  0.0   Min.   : 0.00  
##  Class :character   Class :character   1st Qu.:130.0   1st Qu.: 0.00  
##  Mode  :character   Mode  :character   Median :250.0   Median : 7.00  
##                                        Mean   :257.2   Mean   :10.06  
##                                        3rd Qu.:380.0   3rd Qu.:18.00  
##                                        Max.   :650.0   Max.   :37.00  
##    Carb. (g)       Fiber (g)       Protein (g)    
##  Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.:21.00   1st Qu.: 0.000   1st Qu.: 1.000  
##  Median :35.00   Median : 1.000   Median : 6.000  
##  Mean   :33.97   Mean   : 1.771   Mean   : 8.185  
##  3rd Qu.:45.00   3rd Qu.: 3.000   3rd Qu.:13.000  
##  Max.   :80.00   Max.   :21.000   Max.   :34.000

head(sb_nutr)

## # A tibble: 6 × 7
##   Item         Category Calories `Fat (g)` `Carb. (g)` `Fiber (g)` `Protein (g)`
##   <chr>        <chr>       <dbl>     <dbl>       <dbl>       <dbl>         <dbl>
## 1 Chonga Bagel Food          300         5          50           3            12
## 2 8-Grain Roll Food          380         6          70           7            10
## 3 Almond Croi… Food          410        22          45           3            10
## 4 Apple Fritt… Food          460        23          56           2             7
## 5 Banana Nut … Food          420        22          52           2             6
## 6 Blueberry M… Food          380        16          53           1             6

dim(usa_pop)

## [1] 55  2

colnames(usa_pop)

## [1] "state"      "population"

summary(usa_pop)

##     state             population      
##  Length:55          Min.   :   56882  
##  Class :character   1st Qu.: 1344331  
##  Mode  :character   Median : 3751351  
##                     Mean   : 5677621  
##                     3rd Qu.: 6515716  
##                     Max.   :37253956

head(usa_pop)

## # A tibble: 6 × 2
##   state      population
##   <chr>           <dbl>
## 1 Alabama       4779736
## 2 Alaska         710231
## 3 Arizona       6392017
## 4 Arkansas      2915918
## 5 California   37253956
## 6 Colorado      5029196

dim(usa_states)

## [1] 51  2

colnames(usa_states)

## [1] "State"        "Abbreviation"

summary(usa_states)

##     State           Abbreviation      
##  Length:51          Length:51         
##  Class :character   Class :character  
##  Mode  :character   Mode  :character

head(usa_states)

## # A tibble: 6 × 2
##   State      Abbreviation
##   <chr>      <chr>       
## 1 Alabama    AL          
## 2 Alaska     AK          
## 3 Arizona    AZ          
## 4 Arkansas   AR          
## 5 California CA          
## 6 Colorado   CO

We have checked the varibale names and information that were imported from the four dataset files.

3. Format the data

Subset Starbucks data to the US.
Create counts of Starbucks stores by state.
Merge population in with the store count by state.
Inspect the range values for each variable.

sb_usa <- sb_locs |> filter(
  # keep only in the US
  Country == 'US'
)

sb_locs_state <- sb_usa |>
  group_by(
    `State/Province`
  ) |>
  rename(
    state = `State/Province`
  ) |>
  summarize(
    n_stores = n()
  )

# need state abbreviations
usa_pop_abbr <- 
  full_join(
    usa_pop, usa_states,
    by = join_by(state == State)
            ) 
  
sb_locs_state <- full_join(
  sb_locs_state,
  usa_pop_abbr,
  by = join_by(state == Abbreviation)
)

summary(sb_locs_state)

##     state              n_stores        state.y            population      
##  Length:55          Min.   :   8.0   Length:55          Min.   :   56882  
##  Class :character   1st Qu.:  56.5   Class :character   1st Qu.: 1344331  
##  Mode  :character   Median : 123.0   Mode  :character   Median : 3751351  
##                     Mean   : 266.8                      Mean   : 5677621  
##                     3rd Qu.: 332.0                      3rd Qu.: 6515716  
##                     Max.   :2821.0                      Max.   :37253956  
##                     NA's   :4

4. Use `ggplotly` for EDA

Answer the following questions:

Are the number of Starbucks proportional to the population of a state? (scatterplot)
Is the caloric distribution of Starbucks menu items different for drinks and food? (histogram)
What are the top 20 words in Starbucks menu items? (bar plot)

p1 <- ggplot(sb_locs_state, aes(x = population, y = n_stores, color = state)) +
  geom_point(alpha = 0.8) 

ggplotly(p1)

From the scatterplot, there is a positive association between the population and number of Starbucks stores, i.e., more population leads to more stores.

p2 <- ggplot(sb_nutr, aes(x = Calories, fill = Category)) +
  geom_histogram(alpha = 0.7, binwidth = 50)

ggplotly(p2)

Yes, the calories for food is overall higher than the calories of drink items.

p3<- sb_nutr |>
  unnest_tokens(word, Item, token="words") |>
  count(word, sort=T) |>
  head(20) |>
  ggplot(aes(reorder(word,n), n))+
  geom_col()+
  coord_flip()

ggplotly(p3)

Above showed the top 20 words from the menu such as “iced”.

5. Scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing the relationship between calories and carbs
Color points by category

sb_nutr |>
  plot_ly(x = ~Calories,
          y = ~`Carb. (g)`,
          type = "scatter",
          mode = "markers",
          color = ~Category)

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

Create this scatterplot but for the items consisting of the top 10 words
Color again by category
Add hoverinfo specifying the word in the item name
Add layout information to title the chart and the axes
Enable hovermode = "compare"

topwords<- sb_nutr |>
  unnest_tokens(word, Item, token="words") |>
  count(word, sort=T) |>
  head(10)

sb_nutr |> 
  unnest_tokens(word, Item, token="words") |>
  filter(word %in% topwords$word) |>
  plot_ly(
    x = ~Calories,
    y = ~`Carb. (g)`,
    type = "scatter",
    mode = "markers",
    color = ~Category,
    hoverinfo = "text",
    text = ~paste0("Item: ", word, sep = "")
  ) |>
  layout(title = "Calories vs. Carbs",
         xaxis = list(title = "Calories"),
         yaxis = list(title = "Carbs (g)"),
         hovermode = "compare")

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

6. `plot_ly` Boxplots

Create a boxplot of all of the nutritional variables in groups by the 10 item words.

filtered_data <- sb_nutr |> 
  unnest_tokens(word, Item, token="words") |>
  filter(word %in% topwords$word)

boxplot <- filtered_data |>
  plot_ly(x = ~word, type = "box") |>
  add_boxplot(y = ~Calories, boxpoints="all") |>
  add_boxplot(y = ~`Fat (g)`, boxpoints="all") |>
  add_boxplot(y = ~`Carb. (g)`, boxpoints="all") |>
  add_boxplot(y = ~`Fiber (g)`, boxpoints="all") |>
  add_boxplot(y = ~`Protein (g)`, boxpoints="all") |>
  layout(boxmode = "group")

boxplot

## Warning: 'layout' objects don't have these attributes: 'boxmode'
## Valid attributes include:
## '_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'

7. 3D Scatterplot

Create a 3D scatterplot between Calories, Carbs, and Protein for the items containing the top 10 words
Do you see any patterns?

filtered_data |>
  plot_ly(x = ~Calories,
          y = ~`Carb. (g)`,
          z = ~`Protein (g)`,
          type = 'scatter3d',
          mode = 'markers',
          color = ~word)

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

It seems like the points live on a 2D hyperplane in the 3D space, which means we can describe the differences among these items only by two measures instead of three.

8. `plot_ly` Map

Create a map to visualize the number of stores per state, and another for the population by state. Use subplot to put the maps side by side.
Describe the differences if any.

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('steelblue')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create hover text
sb_locs_state$hover <- with(sb_locs_state, paste("Number of Starbucks: ", n_stores, '<br>', "State: ", state.y, '<br>', "Population: ", population))

# Create the map
map1 <- plot_geo(sb_locs_state, locationmode = 'USA-states') |>
  add_trace(z=~n_stores, text=~hover, locations=~state, color = ~n_stores, colors = 'Purples') |>
  layout(title = 'starbucks stores by state', geo=set_map_details)
map1

## Warning: Ignoring 4 observations

map2 <- plot_geo(sb_locs_state, locationmode = 'USA-states') |>
  add_trace(z = ~population, text = ~hover, locations = ~state, color = ~population,
            colors = 'Purples') |>
  layout(title = 'starbucks stores by population', geo=set_map_details)
map2

subplot(map1, map2)

## Warning: Ignoring 4 observations

There is no significant differences between the two plots. This confirms the intuition from the earlier scatterplot, i.e., more population leads to more stores.

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format the data

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

7. 3D Scatterplot

8. `plot_ly` Map

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format the data

4. Use ggplotly for EDA

5. Scatterplots using plot_ly()

6. plot_ly Boxplots

7. 3D Scatterplot

8. plot_ly Map

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

8. `plot_ly` Map